现代信息科技2025，Vol.9Issue(4)：64-68,73,6.DOI:10.19850/j.cnki.2096-4706.2025.04.013
基于YOLOv9葡萄病害识别检测算法研究
Research on Grape Disease Identification and Detection Algorithm Based on YOLOv9
摘要
Abstract
As one of the latest versions in the YOLO series of models,YOLOv9 features convenient platform transplantation and simple detection procedures.Compared with traditional image recognition technologies,object detection models based on Deep Learning possess stronger feature extraction and generalization capabilities,and can better recognize complex objects and scenes.Based on the research on YOLOv9c grape disease identification and detection algorithm,aiming at the issues such as low recognition accuracy and long processing time existing in traditional disease recognition methods,this paper conducts recognition of seven types of grape diseases in China,and the average detection metric mAP50 reaches 92.7%after training.Experimental results demonstrate that this method can achieve real-time detection of grape diseases,significantly improving agricultural production efficiency and meeting the precision and real-time requirements of grape disease detection application scenarios.关键词
YOLOv9/葡萄病害/实时检测/损失函数/高性能Key words
YOLOv9/grape diseases/real-time detection/loss function/high-performance分类
计算机与自动化引用本文复制引用
萧峥嵘,梁烨锋,李菲,王义宗,田纪亚..基于YOLOv9葡萄病害识别检测算法研究[J].现代信息科技,2025,9(4):64-68,73,6.基金项目
国家级大学生创新创业训练计划项目(202413558003) （202413558003）
2021年度校级项目(ZY202105) （ZY202105）
2023年度校级重点项目(ZZ202303) （ZZ202303）
